Generally, a gyroscope is a sensor measuring angular velocity, which measures the angular velocity of the rotation of an object or a vehicle. In the case of using a gyroscope, only the angular velocity of rotation is substantially used for control, such as an image stabilizer of a camcorder, a three-dimensional mouse, a dynamic controller for an RC helicopter, and an electronic stability program for an automobile.
In the case of using a gyroscope, as described above, many types of errors such as a conversion factor error, a conversion factor nonlinearity, a static bias error, an in-run bias error, a run-to-run bias error, a quantization error, and an unstructured error, noises such as white noise, color noise, dynamic noise, drive noise, and resonance frequency noise, sensitivity such as G sensitivity and G2 sensitivity, and temperature drift such as a conversion factor and bias do not create many problems.
Namely, the various errors are substantially managed in a field requiring a high-accuracy navigation system and are not considered important except for the conversion factor error.
However, currently, a low-priced inertia sensors using microelectric mechanical system (MEMS), whose quality is relatively lower, are becoming commonly used in navigation system such as car navigation and personal navigation in addition to traditional control uses. Therefore, the errors described above start becoming important variables affecting these inertia sensors.
Generally, the navigation system provides information on location recognition such as location, posture, velocity, acceleration, time, angular direction, and angular velocity by using a navigation sensor. Navigation sensors used in conventional navigation systems are generally used in munitions, which are high-priced and have high accuracy. Since the error of the high-priced sensor having high accuracy is kept very small, not much error compensation is required and the effect of error compensation is not relatively large.
However, currently, as MEMS technology becomes more advanced, small and low-priced inertia sensors are developed for civilian use. Though the MEMS type inertia sensor is small and low-priced, it has more various error factors than high-priced sensors, and if the error factors described above can not be adjusted, accuracy of navigation systems can not be obtained. Therefore, error compensation is a very important part of inertia navigation systems using a small/low-priced inertia sensor.
The error factors described above may be largely divided into a deterministic error and a nondeterministic error. The deterministic error related to the present invention is an error whose property is identified for each sensor and can be compensated for in advance by using various error compensation methods. There is a conversion factor error and a static bias error in the deterministic error.
A conversion factor indicates the ratio of an angular velocity according to the output voltage of a gyroscope, and a unit of the conversion factor is deg/sec/V or rad/sec/V. Namely, the conversion factor is a factor converting the value of an angular velocity according to output voltage.
In the conversion factor, a certain error occurs according to a bias voltage or resistance. A corrected conversion factor error indicates a difference between an actual conversion factor and a theoretical conversion factor. A conversion factor error causes all error of degree of angle. In the case of an MEMS sensor, since a conversion factor error is at most 5 to 10% according to sensors, if a conversion factor error according to each sample is not compensated for, a maximum of 5 to 10% of an initial angular error and an accumulated error will occur. As illustrated in FIG. 1, a linear error of the static conversion factor, as described above, is identical with the difference between the gradient of an actually measured value and the gradient of a theoretical value.
On the other hand, as illustrated in FIG. 2, a nonlinear error of the static conversion factor indicates the change of output according to the size of an input angular velocity. The linear error of the static conversion factor is unrelated to the size of the input angular velocity, as illustrated in FIG. 1, and the nonlinear error of the static conversion factor is an error according to the size of the input, as illustrated in FIG. 2.
In conventional low-priced dynamic apparatuses, many conversion factor errors occur due to the change of a resistance or the influence of bias. Also, conventional low-priced dynamics apparatus have a problem in that an error caused by a change of temperature, the passage of time, or a change in power, may occur. In the case the error is not compensated for, the conversion factor error of the dynamic apparatus increases. However, with conventional methods, a method of compensating the conversion factor of the dynamic apparatus in order to precisely perform a rotation can not be provided.
In FIG. 3, a cleaning robot moves in a route shown in FIG. 3 in order to effectively clean a certain area, using a low-priced dynamic apparatus. However, in the case the cleaning robot is using a conventional low-priced dynamic apparatus, if the error of a conversion factor is more than a predetermined standard value, a rotation angular velocity is not outputted. Accordingly, as illustrated in FIG. 4, the cleaning robot rotates at an angle different from a determined rotation angle, thereby not effectively cleaning the area.
As described above, since the error of a conversion factor is more than a predetermined standard value and the accuracy of a rotation angle is lower, a conventional low-priced dynamic apparatus rotates by a rotation angle more or less than a determined rotation angle, thereby does not perform a precise rotation. Particularly, in the case a conversion factor error is accumulated, the problem will inevitably increase.